Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations6150
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory249.4 B

Variable types

Text3
Numeric7

Alerts

% pop >= 25 ug/m3 [%] is highly overall correlated with Geographic-Mean PM2.5 [ug/m3] and 2 other fieldsHigh correlation
Geographic Coverage [%] is highly overall correlated with Population Coverage [%]High correlation
Geographic-Mean PM2.5 [ug/m3] is highly overall correlated with % pop >= 25 ug/m3 [%] and 2 other fieldsHigh correlation
Population Coverage [%] is highly overall correlated with Geographic Coverage [%]High correlation
Population-Weighted PM2.5 [ug/m3] is highly overall correlated with % pop >= 25 ug/m3 [%] and 2 other fieldsHigh correlation
Total Population [million people] is highly overall correlated with % pop >= 25 ug/m3 [%] and 2 other fieldsHigh correlation
Total Population [million people] has 100 (1.6%) zeros Zeros
% pop >= 25 ug/m3 [%] has 3025 (49.2%) zeros Zeros

Reproduction

Analysis started2025-04-06 20:57:25.300687
Analysis finished2025-04-06 20:57:29.373348
Duration4.07 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Region
Text

Distinct246
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size402.7 KiB
2025-04-06T23:57:29.497348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length22
Mean length9.3617886
Min length4

Characters and Unicode

Total characters57575
Distinct characters61
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAruba
2nd rowAruba
3rd rowAruba
4th rowAruba
5th rowAruba
ValueCountFrequency (%)
islands 325
 
3.8%
and 275
 
3.2%
republic 125
 
1.5%
saint 125
 
1.5%
united 75
 
0.9%
guinea 75
 
0.9%
south 75
 
0.9%
of 75
 
0.9%
new 75
 
0.9%
cyprus 50
 
0.6%
Other values (278) 7200
85.0%
2025-04-06T23:57:29.727382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8425
14.6%
n 4800
 
8.3%
i 4675
 
8.1%
e 3850
 
6.7%
r 3275
 
5.7%
o 2975
 
5.2%
2325
 
4.0%
s 2325
 
4.0%
l 2300
 
4.0%
t 2300
 
4.0%
Other values (51) 20325
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57575
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8425
14.6%
n 4800
 
8.3%
i 4675
 
8.1%
e 3850
 
6.7%
r 3275
 
5.7%
o 2975
 
5.2%
2325
 
4.0%
s 2325
 
4.0%
l 2300
 
4.0%
t 2300
 
4.0%
Other values (51) 20325
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57575
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8425
14.6%
n 4800
 
8.3%
i 4675
 
8.1%
e 3850
 
6.7%
r 3275
 
5.7%
o 2975
 
5.2%
2325
 
4.0%
s 2325
 
4.0%
l 2300
 
4.0%
t 2300
 
4.0%
Other values (51) 20325
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57575
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8425
14.6%
n 4800
 
8.3%
i 4675
 
8.1%
e 3850
 
6.7%
r 3275
 
5.7%
o 2975
 
5.2%
2325
 
4.0%
s 2325
 
4.0%
l 2300
 
4.0%
t 2300
 
4.0%
Other values (51) 20325
35.3%

Year
Real number (ℝ)

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010
Minimum1998
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:29.792378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1998
5-th percentile1999
Q12004
median2010
Q32016
95-th percentile2021
Maximum2022
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2116889
Coefficient of variation (CV)0.0035879049
Kurtosis-1.2038492
Mean2010
Median Absolute Deviation (MAD)6
Skewness0
Sum12361500
Variance52.008457
MonotonicityNot monotonic
2025-04-06T23:57:29.861378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1998 246
 
4.0%
2011 246
 
4.0%
2021 246
 
4.0%
2020 246
 
4.0%
2019 246
 
4.0%
2018 246
 
4.0%
2017 246
 
4.0%
2016 246
 
4.0%
2015 246
 
4.0%
2014 246
 
4.0%
Other values (15) 3690
60.0%
ValueCountFrequency (%)
1998 246
4.0%
1999 246
4.0%
2000 246
4.0%
2001 246
4.0%
2002 246
4.0%
2003 246
4.0%
2004 246
4.0%
2005 246
4.0%
2006 246
4.0%
2007 246
4.0%
ValueCountFrequency (%)
2022 246
4.0%
2021 246
4.0%
2020 246
4.0%
2019 246
4.0%
2018 246
4.0%
2017 246
4.0%
2016 246
4.0%
2015 246
4.0%
2014 246
4.0%
2013 246
4.0%

Population-Weighted PM2.5 [ug/m3]
Real number (ℝ)

High correlation 

Distinct648
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.258439
Minimum1.4
Maximum106
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:29.946379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.4
5-th percentile4.5
Q110.2
median17.3
Q326.5
95-th percentile48.155
Maximum106
Range104.6
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation13.99098
Coefficient of variation (CV)0.69062477
Kurtosis3.5704522
Mean20.258439
Median Absolute Deviation (MAD)7.8
Skewness1.5581834
Sum124589.4
Variance195.74751
MonotonicityNot monotonic
2025-04-06T23:57:30.038943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7 42
 
0.7%
4.4 39
 
0.6%
4.3 39
 
0.6%
4.5 38
 
0.6%
4.6 36
 
0.6%
10.4 34
 
0.6%
18.2 33
 
0.5%
10.7 32
 
0.5%
12.1 32
 
0.5%
9.2 32
 
0.5%
Other values (638) 5793
94.2%
ValueCountFrequency (%)
1.4 1
 
< 0.1%
1.5 1
 
< 0.1%
1.6 6
0.1%
1.7 2
 
< 0.1%
1.8 5
0.1%
1.9 5
0.1%
2 4
0.1%
2.1 1
 
< 0.1%
2.5 2
 
< 0.1%
2.6 1
 
< 0.1%
ValueCountFrequency (%)
106 1
< 0.1%
102.1 1
< 0.1%
101.2 1
< 0.1%
100.9 1
< 0.1%
99.3 1
< 0.1%
98.1 1
< 0.1%
97.3 1
< 0.1%
96.5 1
< 0.1%
94.8 1
< 0.1%
94.7 1
< 0.1%

Geographic-Mean PM2.5 [ug/m3]
Real number (ℝ)

High correlation 

Distinct633
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.889772
Minimum1.1
Maximum105.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:30.126943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile4.4
Q19.8
median16.9
Q325.3
95-th percentile48
Maximum105.7
Range104.6
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation13.963481
Coefficient of variation (CV)0.70204329
Kurtosis3.0352467
Mean19.889772
Median Absolute Deviation (MAD)7.5
Skewness1.5062927
Sum122322.1
Variance194.97881
MonotonicityNot monotonic
2025-04-06T23:57:30.220041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4 46
 
0.7%
4.3 43
 
0.7%
4.7 43
 
0.7%
4.8 41
 
0.7%
4.6 40
 
0.7%
4.5 36
 
0.6%
4.9 36
 
0.6%
10.1 36
 
0.6%
16.9 35
 
0.6%
9.2 35
 
0.6%
Other values (623) 5759
93.6%
ValueCountFrequency (%)
1.1 2
 
< 0.1%
1.2 6
0.1%
1.3 8
0.1%
1.4 6
0.1%
1.5 1
 
< 0.1%
1.7 2
 
< 0.1%
2.5 2
 
< 0.1%
2.8 6
0.1%
2.9 3
 
< 0.1%
3 4
0.1%
ValueCountFrequency (%)
105.7 1
< 0.1%
102.2 1
< 0.1%
97.2 1
< 0.1%
96 1
< 0.1%
94.5 1
< 0.1%
93.9 1
< 0.1%
93.5 1
< 0.1%
93.3 1
< 0.1%
90.3 1
< 0.1%
89.2 1
< 0.1%

Population Coverage [%]
Real number (ℝ)

High correlation 

Distinct54
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.582943
Minimum68.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:30.310044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum68.2
5-th percentile99.5
Q199.9
median100
Q3100
95-th percentile100
Maximum100
Range31.8
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation2.7229024
Coefficient of variation (CV)0.02734306
Kurtosis89.651057
Mean99.582943
Median Absolute Deviation (MAD)0
Skewness-9.1609303
Sum612435.1
Variance7.4141975
MonotonicityNot monotonic
2025-04-06T23:57:30.403550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3979
64.7%
99.9 1149
 
18.7%
99.8 341
 
5.5%
99.7 191
 
3.1%
99.6 115
 
1.9%
99.1 73
 
1.2%
99.5 72
 
1.2%
98.7 35
 
0.6%
68.2 25
 
0.4%
99.2 24
 
0.4%
Other values (44) 146
 
2.4%
ValueCountFrequency (%)
68.2 25
0.4%
74.1 3
 
< 0.1%
74.8 1
 
< 0.1%
75.4 1
 
< 0.1%
76 1
 
< 0.1%
76.6 1
 
< 0.1%
77.1 1
 
< 0.1%
77.8 1
 
< 0.1%
78.4 1
 
< 0.1%
78.9 1
 
< 0.1%
ValueCountFrequency (%)
100 3979
64.7%
99.9 1149
 
18.7%
99.8 341
 
5.5%
99.7 191
 
3.1%
99.6 115
 
1.9%
99.5 72
 
1.2%
99.4 3
 
< 0.1%
99.3 3
 
< 0.1%
99.2 24
 
0.4%
99.1 73
 
1.2%

Geographic Coverage [%]
Real number (ℝ)

High correlation 

Distinct48
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.2
Minimum1.7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:30.497551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.7
5-th percentile95
Q199.2
median99.8
Q3100
95-th percentile100
Maximum100
Range98.3
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation8.2522438
Coefficient of variation (CV)0.08403507
Kurtosis88.705043
Mean98.2
Median Absolute Deviation (MAD)0.2
Skewness-8.9298479
Sum603930
Variance68.099528
MonotonicityNot monotonic
2025-04-06T23:57:30.588992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
100 2075
33.7%
99.9 850
13.8%
99.8 550
 
8.9%
99.7 275
 
4.5%
99.4 275
 
4.5%
99.6 225
 
3.7%
99.3 175
 
2.8%
99.2 150
 
2.4%
99.5 150
 
2.4%
99 75
 
1.2%
Other values (38) 1350
22.0%
ValueCountFrequency (%)
1.7 25
0.4%
42.6 25
0.4%
45 25
0.4%
80 25
0.4%
81.3 25
0.4%
86.7 25
0.4%
90.5 25
0.4%
90.9 25
0.4%
93.3 25
0.4%
93.7 25
0.4%
ValueCountFrequency (%)
100 2075
33.7%
99.9 850
13.8%
99.8 550
 
8.9%
99.7 275
 
4.5%
99.6 225
 
3.7%
99.5 150
 
2.4%
99.4 275
 
4.5%
99.3 175
 
2.8%
99.2 150
 
2.4%
99.1 75
 
1.2%

Total Population [million people]
Real number (ℝ)

High correlation  Zeros 

Distinct3709
Distinct (%)60.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.031638
Minimum0
Maximum1398.52
Zeros100
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:30.700991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.005
Q10.2175
median4.186
Q316.99775
95-th percentile93.203
Maximum1398.52
Range1398.52
Interquartile range (IQR)16.78025

Descriptive statistics

Standard deviation119.91563
Coefficient of variation (CV)4.2778674
Kurtosis97.478083
Mean28.031638
Median Absolute Deviation (MAD)4.1505
Skewness9.5345053
Sum172394.57
Variance14379.759
MonotonicityNot monotonic
2025-04-06T23:57:30.993386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.002 125
 
2.0%
0 100
 
1.6%
0.004 65
 
1.1%
0.012 49
 
0.8%
0.005 44
 
0.7%
0.011 41
 
0.7%
0.023 26
 
0.4%
0.044 26
 
0.4%
0.013 24
 
0.4%
0.099 24
 
0.4%
Other values (3699) 5626
91.5%
ValueCountFrequency (%)
0 100
1.6%
0.002 125
2.0%
0.003 2
 
< 0.1%
0.004 65
1.1%
0.005 44
 
0.7%
0.006 7
 
0.1%
0.007 7
 
0.1%
0.008 20
 
0.3%
0.009 10
 
0.2%
0.01 4
 
0.1%
ValueCountFrequency (%)
1398.52 3
< 0.1%
1393.215 1
 
< 0.1%
1387.91 1
 
< 0.1%
1386.215 3
< 0.1%
1382.604 1
 
< 0.1%
1377.299 1
 
< 0.1%
1371.993 1
 
< 0.1%
1370.661 1
 
< 0.1%
1365.032 1
 
< 0.1%
1358.071 1
 
< 0.1%

% pop >= 25 ug/m3 [%]
Real number (ℝ)

High correlation  Zeros 

Distinct860
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.029431
Minimum0
Maximum100
Zeros3025
Zeros (%)49.2%
Negative0
Negative (%)0.0%
Memory size48.2 KiB
2025-04-06T23:57:31.087396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.1
Q354.2
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)54.2

Descriptive statistics

Standard deviation38.409951
Coefficient of variation (CV)1.4210418
Kurtosis-0.6739349
Mean27.029431
Median Absolute Deviation (MAD)0.1
Skewness1.0207629
Sum166231
Variance1475.3243
MonotonicityNot monotonic
2025-04-06T23:57:31.184512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3025
49.2%
100 500
 
8.1%
0.1 64
 
1.0%
99.9 52
 
0.8%
0.2 41
 
0.7%
0.3 32
 
0.5%
0.4 25
 
0.4%
99.7 25
 
0.4%
99.8 20
 
0.3%
0.5 20
 
0.3%
Other values (850) 2346
38.1%
ValueCountFrequency (%)
0 3025
49.2%
0.1 64
 
1.0%
0.2 41
 
0.7%
0.3 32
 
0.5%
0.4 25
 
0.4%
0.5 20
 
0.3%
0.6 18
 
0.3%
0.7 19
 
0.3%
0.8 11
 
0.2%
0.9 14
 
0.2%
ValueCountFrequency (%)
100 500
8.1%
99.9 52
 
0.8%
99.8 20
 
0.3%
99.7 25
 
0.4%
99.6 16
 
0.3%
99.5 17
 
0.3%
99.4 13
 
0.2%
99.3 8
 
0.1%
99.2 12
 
0.2%
99.1 13
 
0.2%
Distinct246
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size398.5 KiB
2025-04-06T23:57:31.345048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length24
Mean length9.3252033
Min length4

Characters and Unicode

Total characters57350
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaruba
2nd rowaruba
3rd rowaruba
4th rowaruba
5th rowaruba
ValueCountFrequency (%)
islands 325
 
3.8%
and 275
 
3.2%
republic 125
 
1.5%
saint 125
 
1.5%
united 75
 
0.9%
guinea 75
 
0.9%
south 75
 
0.9%
of 75
 
0.9%
new 75
 
0.9%
cyprus 50
 
0.6%
Other values (278) 7200
85.0%
2025-04-06T23:57:31.570561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8950
15.6%
i 5325
 
9.3%
n 5225
 
9.1%
e 4125
 
7.2%
r 3550
 
6.2%
s 3350
 
5.8%
o 3025
 
5.3%
t 2700
 
4.7%
l 2625
 
4.6%
u 2375
 
4.1%
Other values (17) 16100
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57350
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8950
15.6%
i 5325
 
9.3%
n 5225
 
9.1%
e 4125
 
7.2%
r 3550
 
6.2%
s 3350
 
5.8%
o 3025
 
5.3%
t 2700
 
4.7%
l 2625
 
4.6%
u 2375
 
4.1%
Other values (17) 16100
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57350
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8950
15.6%
i 5325
 
9.3%
n 5225
 
9.1%
e 4125
 
7.2%
r 3550
 
6.2%
s 3350
 
5.8%
o 3025
 
5.3%
t 2700
 
4.7%
l 2625
 
4.6%
u 2375
 
4.1%
Other values (17) 16100
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57350
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8950
15.6%
i 5325
 
9.3%
n 5225
 
9.1%
e 4125
 
7.2%
r 3550
 
6.2%
s 3350
 
5.8%
o 3025
 
5.3%
t 2700
 
4.7%
l 2625
 
4.6%
u 2375
 
4.1%
Other values (17) 16100
28.1%
Distinct195
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size360.5 KiB
2025-04-06T23:57:31.776304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters18450
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaru
2nd rowaru
3rd rowaru
4th rowaru
5th rowaru
ValueCountFrequency (%)
sai 175
 
2.8%
nor 125
 
2.0%
mal 125
 
2.0%
mon 100
 
1.6%
gua 75
 
1.2%
bel 75
 
1.2%
sou 75
 
1.2%
gre 75
 
1.2%
uni 75
 
1.2%
tur 75
 
1.2%
Other values (185) 5175
84.1%
2025-04-06T23:57:32.052303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2375
12.9%
r 1475
 
8.0%
i 1325
 
7.2%
e 1275
 
6.9%
n 1225
 
6.6%
u 1200
 
6.5%
o 1175
 
6.4%
s 1100
 
6.0%
m 1050
 
5.7%
l 850
 
4.6%
Other values (17) 5400
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2375
12.9%
r 1475
 
8.0%
i 1325
 
7.2%
e 1275
 
6.9%
n 1225
 
6.6%
u 1200
 
6.5%
o 1175
 
6.4%
s 1100
 
6.0%
m 1050
 
5.7%
l 850
 
4.6%
Other values (17) 5400
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2375
12.9%
r 1475
 
8.0%
i 1325
 
7.2%
e 1275
 
6.9%
n 1225
 
6.6%
u 1200
 
6.5%
o 1175
 
6.4%
s 1100
 
6.0%
m 1050
 
5.7%
l 850
 
4.6%
Other values (17) 5400
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2375
12.9%
r 1475
 
8.0%
i 1325
 
7.2%
e 1275
 
6.9%
n 1225
 
6.6%
u 1200
 
6.5%
o 1175
 
6.4%
s 1100
 
6.0%
m 1050
 
5.7%
l 850
 
4.6%
Other values (17) 5400
29.3%

Interactions

2025-04-06T23:57:28.696899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.484649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.987649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.497992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.987821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.682139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.170861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.763898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.547648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.053650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.566992image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.056822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.749140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.242861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.834899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.620648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.123649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.639822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.129206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.823141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.316863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.901898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.687648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.206648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.706823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.201206image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.891140image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.393865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.975897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.757648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.281653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.775823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.269208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.962345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.469862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:29.043370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.830650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.352990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.844823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.538207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.028344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.543861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:29.116883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:25.905652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.426989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:26.918822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:27.613207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.103344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-06T23:57:28.619863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-06T23:57:32.107309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
% pop >= 25 ug/m3 [%]Geographic Coverage [%]Geographic-Mean PM2.5 [ug/m3]Population Coverage [%]Population-Weighted PM2.5 [ug/m3]Total Population [million people]Year
% pop >= 25 ug/m3 [%]1.000-0.0160.8980.0470.9130.527-0.012
Geographic Coverage [%]-0.0161.0000.1040.5670.080-0.0680.000
Geographic-Mean PM2.5 [ug/m3]0.8980.1041.0000.1430.9780.547-0.027
Population Coverage [%]0.0470.5670.1431.0000.137-0.0010.007
Population-Weighted PM2.5 [ug/m3]0.9130.0800.9780.1371.0000.582-0.022
Total Population [million people]0.527-0.0680.547-0.0010.5821.0000.037
Year-0.0120.000-0.0270.007-0.0220.0371.000

Missing values

2025-04-06T23:57:29.219348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-06T23:57:29.309347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

RegionYearPopulation-Weighted PM2.5 [ug/m3]Geographic-Mean PM2.5 [ug/m3]Population Coverage [%]Geographic Coverage [%]Total Population [million people]% pop >= 25 ug/m3 [%]Country_normalizedcountry_prefix
0Aruba19989.59.3100.0100.00.0880.0arubaaru
1Aruba199910.19.9100.0100.00.0880.0arubaaru
2Aruba200010.210.0100.0100.00.0880.0arubaaru
3Aruba200110.610.4100.0100.00.0900.0arubaaru
4Aruba200210.510.3100.0100.00.0920.0arubaaru
5Aruba200311.411.2100.0100.00.0930.0arubaaru
6Aruba200410.19.9100.0100.00.0950.0arubaaru
7Aruba200510.410.2100.0100.00.0970.0arubaaru
8Aruba200610.410.2100.0100.00.0970.0arubaaru
9Aruba200710.710.5100.0100.00.0980.0arubaaru
RegionYearPopulation-Weighted PM2.5 [ug/m3]Geographic-Mean PM2.5 [ug/m3]Population Coverage [%]Geographic Coverage [%]Total Population [million people]% pop >= 25 ug/m3 [%]Country_normalizedcountry_prefix
6140Zimbabwe201317.316.7100.099.414.9400.0zimbabwezim
6141Zimbabwe201415.314.4100.099.415.2650.0zimbabwezim
6142Zimbabwe201517.917.2100.099.415.5910.0zimbabwezim
6143Zimbabwe201615.815.0100.099.415.9640.0zimbabwezim
6144Zimbabwe201715.715.0100.099.416.3380.0zimbabwezim
6145Zimbabwe201818.117.3100.099.416.7110.0zimbabwezim
6146Zimbabwe201916.615.6100.099.417.0850.0zimbabwezim
6147Zimbabwe202013.813.3100.099.417.4580.0zimbabwezim
6148Zimbabwe202115.314.5100.099.417.4580.0zimbabwezim
6149Zimbabwe202217.516.0100.099.417.4580.0zimbabwezim